There is a need to understand how the brain responds and adapts to losses of balance and missteps during walking as we age. This knowledge could help improve fall interventions and advance gait rehabilitation therapies. We propose to use electroencephalography (EEG) and independent components analysis (ICA) to identify and quantify brain responses to perturbations during walking and recumbent stepping, a locomotor task often used in clinics. We will test healthy young and older adults while we record their brain activity using EEG, muscle activity using electromyography (EMG), and body kinematics using motion capture as we perturb their stepping pattern. The perturbations will create stepping errors that will drive adaptation because people often update movements to minimize movement errors. We will use a typical motor adaptation protocol. For Aim 1, we will determine the electrocortical correlates of adapting to perturbations applied during rhythmic lower limb stepping on a recumbent stepper. We will use a robotic recumbent stepper to apply brief resistive force perturbations during specific instances in the stepping cycle. We hypothesize that A) a distributed network of brain regions is involved and includes the anterior cingulate, a brain structure associated with error monitoring; B) young and older adults will reduce stepping errors indicating that they adapted to the perturbations with repeated practice, and brain processes will have larger spectral fluctuations and shift to begin prior to the perturbation during perturbed stepping compared to unperturbed stepping; and C) older adults will use greater muscle coactivation, adapt less well, and have smaller and delayed spectral fluctuations of brain processes compared to young adults. For Aim 2, we will determine the electrocortical correlates of adapting to perturbations applied during walking. We will use a treadmill that can simulate slips and trips in the mediolateral (side-to-side) and anterior-posterior (forwards/backwards) directions to create perturbations during specific instances in the gait cycle. To address potential movement artifact concerns that may be created by the perturbations, we will first block the electrophysiological signals and record isolated movement artifact using the EEG system to characterize the movement artifact in our setup and protocol. This knowledge will help with the analysis and interpretation of the scalp EEG data and may help develop algorithms to remove the movement artifact from EEG signals. In addition to the hypotheses in Aim 1, we have specific hypotheses related to balance control during walking. We hypothesize that the left sensorimotor cortex will have larger spectral fluctuations during perturbed walking compared to unperturbed walking and will be more sensitive to mediolateral perturbations compared to anterior-posterior perturbations. The results of the proposed work will advance our knowledge of brain function in young and older adults by determining adaptation of electrocortical responses to perturbations during walking and a locomotor task. These findings could be applied to develop new fall interventions and gait rehabilitation therapies based on brain dynamics.